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Spatial variability clustering for spatially dependent functional data

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Abstract

This paper introduces a method for clustering spatially dependent functional data. The idea is to consider the contribution of each curve to the spatial variability. Thus, we define a spatial dispersion function associated to each curve and perform a k-means like clustering algorithm. The algorithm is based on the optimization of a fitting criterion between the spatial dispersion functions associated to each curve and the representative of the clusters. The performance of the proposed method is illustrated by an application on real data and a simulation study.

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Authors and Affiliations

  1. Second University of Naples, Caserta, Italy

    Elvira Romano, Antonio Balzanella & Rosanna Verde

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  1. Elvira Romano
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  2. Antonio Balzanella
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  3. Rosanna Verde
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Correspondence to Antonio Balzanella.

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Romano, E., Balzanella, A. & Verde, R. Spatial variability clustering for spatially dependent functional data. Stat Comput 27, 645–658 (2017). https://doi.org/10.1007/s11222-016-9645-2

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  • DOI: https://doi.org/10.1007/s11222-016-9645-2

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